Current prosthetic devices, such as prosthetic hands, are often controlled by electromyogram (EMG) muscle signals from an amputee who is fitted with the prosthetic device. The EMG signals are generally measured on the surface of the skin of the amputee and used for proportional control of various motors used to actuate various functions of the prosthetic device. That is, the voltage related to the muscle contraction defined by the EMG signal is measured, using suitable sensors, and then processed to control various motors or actuators to move the prosthetic device in a desired manner. For example, in the case of an amputee having a transradial or transcarpal amputation, one EMG pre-amplifier may be placed on the anterior compartment of the forearm, and a second EMG pre-amplifier is placed on the posterior compartment of the forearm. The signals from these two antagonistic muscles are amplified, filtered, rectified, and then given opposite algebraic signs, so that activation of the extensor muscles causes the prosthetic hand to open, while activation of the flexor muscles causes the prosthetic hand to close.
Although many different approaches to EMG signal processing have been developed, few have been commercially available for prosthetic devices. For example, numerous EMG signal processing techniques have been proposed for use in prosthetic devices, including: feature extraction, neural networks, and wavelet transforms. Such techniques have been previously utilized to classify EMG signal patterns and to obtain greater accuracy in decoding the amputee's intended movement of the prosthetic device. Unfortunately, these techniques have several drawbacks, including the inability to provide the amputee with control over both position and force of the prosthetic device. For example, in one attempt to provide EMG processing for a prosthetic hand, individual prosthetic finger movements were able to be discerned with a 98% accuracy, but required 32 surface-EMG electrodes to be placed on the forearm of the amputee to attain such performance. Nonlinear control methods produce increased time delays in processing EMG signals and may also require a higher number of EMG electrodes, which are required to be triggered by the amputee's muscle control. Thus, it would be desirable to provide a control scheme to control a prosthetic device, such as a prosthetic hand, which can automate many functions of the prosthetic device, so as to reduce the cognitive burden of the amputee.
The design and control of a prosthetic device, such as a prosthetic hand, is very difficult, and while many advances have been made, the difference in performance between the human hand and the prosthetic hand is substantial. Furthermore, amputees generally desire that their prostheses function in an increasingly more natural and life-like manner that they can control intuitively. In fact, it is common for an amputee to become discouraged and reject the use of his or her prosthesis because of its minimal functionality and lack of intuitive operation.
In addition, current generation prostheses, such as hand prostheses, typically permit only one degree of freedom (DOF) or one function to be controlled, such that in the case of a hand prosthesis, it operates as a gripper that can only open or close to perform a pinch/grasp function. That is, current prosthetic devices require the use of two electromyogram (EMG) signals to control a single DOF or function of the prosthesis at a time. As a result, such current generation prostheses are unable to control multiple DOFs simultaneously, which substantially reduces the dexterity that prosthetic devices, particularly prosthetic hands, can achieve.
In contrast to prosthetic hands, robotic hand technology has progressed further and is much more sophisticated in its operation. For example, the GIFU Hand and the Shadow Robot Hand offer significantly more controllable joints and feedback signals than prosthetic hands. This is because robotic hands are not limited by the numerous design constraints that are imposed by prosthetic hands, which include the requirement that the prosthetic be low mass, have a highly robust mechanical design, be low cost, and have an intuitive human-machine control interface. However, most critically, the lack of an intuitive control system for which to control multiple DOFs of a prosthesis is a critical obstacle that prevents the technology of dexterous robotic hands from being integrated into prosthetic hands.
Therefore, there is a need for a biomimetic controller for a prosthetic device that enables multiple degrees of freedom (DOF) to be simultaneously controlled using at least two EMG signals. In addition, there is a need for a biomimetic controller and EMG signal interpretation algorithms that allow a prosthetic device to be controlled intuitively in a natural, physiologically expected manner.